SkillsVote: Lifecycle Governance of Agent Skills from Collection, Recommendation to Evolution

TL;DR

SkillsVote is a lifecycle-governance framework that manages agent skills from collection to evolution, improving agent performance by filtering and updating skills based on structured evaluation.

Contribution

It introduces a comprehensive framework for managing agent skills throughout their lifecycle, including collection, recommendation, and evolution, with a focus on verifiability and structured updates.

Findings

Offline evolution improves GPT-5.2 performance by up to 7.9 percentage points.

Online evolution enhances SWE-Bench Pro performance by up to 2.6 percentage points.

Governed external skill libraries can boost agent capabilities without model retraining.

Abstract

Long-horizon LLM agents leave traces that could become reusable experience, but raw trajectories are noisy and hard to govern. We treat Agent Skills as an experience schema that couples executable scripts, with non-executable guidance on procedures. Yet open skill ecosystems contain redundant, uneven, environment-sensitive artifacts, and indiscriminate updates can pollute future context. We present SkillsVote, a lifecycle-governance framework for Agent Skills from collection and recommendation to evolution. SkillsVote profiles a million-scale open-source corpus for environment requirements, quality, and verifiability, then synthesizes tasks for verifiable skills. Before execution, SkillsVote performs agentic library search over structured skill library to expose instructional skill context. After execution, it decomposes trajectories into skill-linked subtasks, attributes outcomes to skill use, agent exploration, environment, and result signals, and admits only successful reusable discoveries to evidence-gated updates. In our evaluation, offline evolution improves GPT-5.2 on Terminal-Bench 2.0 by up to 7.9 pp, while online evolution improves SWE-Bench Pro by up to 2.6 pp. Overall, governed external skill libraries can improve frozen agents without model updates when systems control exposure, credit, and preservation.